1. Field
The embodiments described below relate generally to systems for importing operations data into a class-based model.
2. Discussion
Conventional industrial systems often rely to some extent on computer-based automation and monitoring. In some examples of automation and monitoring, data arising from the operation of a manufacturing plant is acquired, analyzed and responded to if necessary. The data may arise from independent sources, with each source configured to provide substantially raw or native “point” data at pre-defined intervals in real or near real-time. The point data may be presented to an operator in real or near-real time, and may include such as numerical values produced by gauges and/or monitors (e.g., speed, temperature, or pressure).
Examples of systems that may acquire, analyze, and act on point data include industrial automation systems, supervisory control and data acquisition (SCADA) systems, and general data acquisition systems. In such systems, point data may be associated with a “tag” to create a structural data element that is made accessible to other components, systems, applications and/or users. In general, point data obtained from selected sources is subject to dynamic change and is monitored and reported through various operations and functions associated with processing the point data. In industrial automation and control systems, decision support and reporting capabilities may be provided based on tag-associated point data that is monitored over very short timeframes ranging in the sub-second to sub-minute range.
Many conventional systems provide only limited capabilities to access, interpret, and/or manipulate tag-based point data collectively or in connection with “non-point” data. Non-point data relates to a broad category of context-providing information that is associated with point data and may extend the functionality and meaning of the point data. Non-point data may include descriptive and/or attribute information characterizing the point data, as well as, other information such as limits, ranges, etc. In conventional systems, integral and flexible manipulation of tag-based point data and non-point data is restricted due to the inherent differences between and properties of the two types of data.
Conventional systems also possess a limited ability to integrate and relate tag-based point data and non-tag-based data. Non-tag-based data may originate from numerous sources and relate to disparate aspects of an enterprise environment. For example, non-tag-based data may comprise data associated with conventional database applications/environments and include transactional information, production data, business data, etc. Conventionally, attempts to integrate non-tag-based data with tag-based point data may be hindered or prevented completely as a consequence of underlying differences in structure and content between these data types. As a result, generating and implementing logical constructions or schema in which both tag-based data and non-tag-based data are integrally used is problematic in conventional systems. Such limitations limit overall flexibility and increase the difficulty of scaling to complex, enterprise-level environments.
The foregoing difficulties in managing tag-based point data, non-point data, and non-tag-based data also hinder efficient access to such data. For example, a data storage system typically provides one or more mechanisms by which an external system may access the data stored therein. A database management system is a mechanism that may be capable of receiving and responding to queries that comply with a standardized query language. An external system may therefore issue such queries to access or to otherwise manipulate data associated with the database management system.
Some data storage systems support proprietary mechanisms for accessing their stored data. Accordingly, if access to the stored data is desired, external systems are limited to using the proprietary mechanisms. Some proprietary mechanisms provide external systems with rather comprehensive access capabilities. However, the capabilities of such proprietary mechanisms are inherently limited in comparison to standardized, open mechanisms such as Structured Query Language (SQL). In either of the foregoing cases, inefficiencies in accessing the data are exacerbated if the data itself is not coherently and effectively managed.